Gas/particle mass transfer process plays an important role in determining aerosol mass concentrations and shaping aerosol size distribution. Its treatments in three dimensional (3-D) Air Quality Models (AQMs), however, are largely uncertain. In this thesis work, the gas/particle mass transfer approaches in an aerosol module are improved and evaluated to identify an accurate yet computationally efficient approach for use in 3-D AQMs. The aerosol module with the improved gas/particle mass transfer approaches has been incorporated into a state-of-science air quality forecasting (AQF) system and evaluated with two 3-D applications.
Several stand alone condensation schemes used in AQMs are first evaluated with a hypothetical condensation-only case. The original formulation of the Bott scheme as implemented in several AQMs is found to be subject to upstream diffusion thus does not warrant continuous use without modifications. The analytical predictor of condensation with a moving center approach (APC_MC) is shown to be more accurate than the Bott and Trajectory-Grid (T-G) condensation schemes, thus has been incorporated into the Model of Aerosol Dynamics, Reaction, Ionization and Dissolution (MADRID) to solve the gas/particle mass transfer process explicitly. The improved hybrid (i.e., hybrid/APC_MC) and kinetic (i.e., kinetic/APC_MC) approaches and the pre-existing bulk equilibrium approach in MADRID are tested using observational data. The hybrid/APC_MC and kinetic/APC_MC are recommended for 3-D applications due to the best compromise between accuracy and computational efficiency.
The improved MADRID has been incorporated into WRF/Chem (referred to as WRF/Chem-MADRID hereafter). WRF/Chem-MADRID with three gas/particle mass transfer approaches (i.e., bulk equilibrium (EQUI), hybrid/APC_MC (HYBR), and kinetic/APC_MC (KINE)) has been tested and evaluated with a 5-day episode from the TexAQS-2000. WRF/Chem-MADRID simulates meteorological parameters fairly well. Simulated hourly O3 shows a high correlation coefficient (0.83) with observations and the overall bias is about -1.8 ppb. Some daily peak O3 mixing ratios are underpredicted, which is possibly due to uncertainties in emissions, inaccurate predictions of small scale meteorological processes, and missing of an OH source and chlorine chemistry in the gas phase mechanism.
WRF/Chem-MADRID (EQUI), (HYBR), and (KINE) overpredict PM2.5 by 37.1%, 35.8%, and 36.5%, respectively. Major differences in simulation results by three gas/particle mass transfer approaches occur over coastal areas, where WRF/Chem-MADRID (EQUI) predicts higher PM2.5 concentrations than those predicted by WRF/Chem-MADRID (HYBR) and (KINE) due to improperly redistributing condensed nitrate from the chloride depletion process to fine mode. In comparison, WRF/Chem-MADRID (KINE) correctly predicts chloride depletion process. WRF/Chem-MADRID (HYBR) predicts chloride depletion process correctly for the last two sections (sections 7 and 8), which are solved by the kinetic approach, while the predictions for section 6 may be still biased due to the use of bulk equilibrium approach. In addition to its surface concentration, the column abundance of aerosol is also evaluated. WRF/Chem-MADRID captures the regional-scale AOD distribution and its day-to-day variability while biases exist over certain areas.
For the application to the 2004 NEAQS episode, WRF/Chem-MADRID gives comparable overall O3 performance as other AQMs and better O 3 performance than some other AQM over certain areas possibly due to the more realistic convective mixing treatment in the model. WRF/Chem-MADRID (HYBR) and WRF/Chem-MADRID (KINE) show better skill than WRF/Chem-MADRID (EQUI) in terms of nitrate predictions over coastal areas. Model simulations confirmed that NEI99 v3 overestimates the actual emissions in 2004, particularly over urban areas.
|School:||North Carolina State University|
|School Location:||United States -- North Carolina|
|Source:||DAI-B 71/02, Dissertation Abstracts International|
|Subjects:||Meteorology, Atmospheric sciences|
|Keywords:||Aerosols, Air quality, Condensation, Mass transfer, Weather forecasting|
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